class_docstr: Construct an MBART tokenizer.
  
  Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
  [SentencePiece](https://github.com/google/sentencepiece).
  
  The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
  <tokens> <eos>` for target language documents.
  
  Examples:
  
  ```python
  >>> from transformers import MBartTokenizer
  
  >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO")
  >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
  >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
  >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
  ```
docstr: Converts a token (str) in an id using the vocab.
docstr_tok: ["Converts","a","token","(","str",")","in","an","id","using","the","vocab","."]
code: def _convert_token_to_id(self, token):
      """Converts a token (str) in an id using the vocab."""
      if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
      spm_id = self.sp_model.PieceToId(token)
  
      # Need to return unknown token if the SP model returned 0
      return (
          spm_id + self.fairseq_offset if spm_id else self.unk_token_id
      )
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 13 for calling the MBartTokenizer obj's underlying member method code with example usage: obj._convert_token_to_id(token) and returns: unknown, self